Salesforce Data 360 Integrations: Complete Guide to Connecting Enterprise Data 2026
⏱ 5 min read
Learn how Salesforce Data 360 integrations connect CRM, databases, data warehouses, marketing platforms, enterprise applications, and other data sources to create unified, actionable customer data for analytics, automation, personalization, and AI.
Important terminology update: Salesforce Data Cloud was rebranded as Salesforce Data 360 on October 14, 2025. Salesforce states that although the name changed, the functionality and content remained unchanged. You may therefore still encounter older references to “Salesforce Data Cloud” across documentation and existing implementations.
This guide uses Data 360 as the current product name while retaining “Salesforce Data Cloud” where it is useful for legacy searches and existing implementations.
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Salesforce Data 360 Integrations: Quick Answer
Connect
Bring Data Together
Data 360 can connect structured and unstructured data from Salesforce and external systems through connectors, integrations, APIs, and zero-copy approaches.
Harmonize
Create Consistent Data
Data can be transformed and mapped to a common model so information from different sources can be interpreted consistently.
Unify
Build Customer Context
Identity resolution and unified profiles help organizations connect customer information across touchpoints and systems.
Activate
Put Data to Work
Unified data can support segmentation, analytics, workflows, personalization, downstream applications, and AI experiences.
Building a Data Foundation for AI?
Before connecting more data sources, establish the architecture, governance, integration strategy, and data quality foundation required for reliable AI and automation.
What Is Salesforce Data 360?
Salesforce Data 360, formerly Salesforce Data Cloud, is Salesforce’s data platform for connecting, preparing, harmonizing, unifying, analyzing, and activating data across Salesforce and external systems.
Salesforce describes Data 360 as a platform that can work with structured and unstructured data, unify customer information, create insights, support personalization, and provide context for Agentforce and other Salesforce experiences.
Data Cloud vs. Data 360
Salesforce Data Cloud and Salesforce Data 360 refer to the same Salesforce data platform across the rebranding transition. Salesforce’s documentation explicitly states that Data Cloud was rebranded to Data 360 in October 2025 and that the functionality and content remained unchanged.
Why Are Salesforce Data 360 Integrations Important?
Enterprise data rarely lives in one application. Customer information can be distributed across CRM systems, ERP platforms, data warehouses, websites, marketing platforms, support applications, commerce systems, databases, and legacy applications.
Data 360 integrations provide a framework for connecting these sources so organizations can work with more complete and consistent data instead of relying on disconnected systems.
Eliminate Data Silos
Connect information distributed across CRM, ERP, warehouses, applications, and other systems.
Improve Customer Context
Bring relevant customer, engagement, transaction, and behavioral information together.
Support AI
Provide trusted enterprise data and context for Agentforce and AI-powered experiences.
Activate Data
Use unified data for segmentation, workflows, analytics, personalization, and downstream applications.
How Do Salesforce Data 360 Integrations Work?
Data 360 integrations generally follow a lifecycle that starts with connecting data sources and continues through ingestion or federation, transformation, harmonization, identity resolution, analysis, and activation.
01
Connect
Connect Salesforce and external data sources.
02
Ingest
Bring data into Data 360 or use supported zero-copy approaches.
03
Harmonize
Transform and map information into a consistent data model.
04
Unify
Resolve identities and create unified customer profiles.
05
Activate
Use the resulting data in applications, analytics, workflows, and AI.
Salesforce’s current Data 360 integration documentation describes connectors, connector services, integrations, enterprise data, and strategic partners as ways to connect and share data. Salesforce also documents both ingestion and zero-copy data federation approaches.
Types of Salesforce Data 360 Integrations
Salesforce Cloud Integrations
Connect data from Salesforce applications such as Sales, Service, Commerce, and Marketing-related sources.
Data Warehouse Integrations
Connect enterprise data platforms and warehouses to make external data available for analysis and activation.
Database Integrations
Connect structured data from databases and operational systems using supported connectors and integration methods.
Marketing Integrations
Connect engagement and marketing data to support audience segmentation, personalization, analytics, and activation.
Zero-Copy Integrations
Use supported zero-copy approaches to work with data in external platforms without creating a traditional duplicate data pipeline.
API and Custom Integrations
Use APIs, SDKs, or custom integration patterns where prebuilt connectors do not fully meet business requirements.
Salesforce’s current connector catalog includes structured and unstructured sources and supports methods such as batch and zero-copy for selected connectors.
Salesforce Data 360 Connectors and Integrations
Salesforce maintains a growing catalog of Data 360 connectors for bringing data into the platform and, depending on the connector, sharing or activating data outward. Available connector capabilities vary by source.
The exact connectors and capabilities change over time, so integration architecture should be validated against Salesforce’s current Data 360 connector catalog before implementation.
Salesforce Data 360 Zero-Copy Integrations
Zero-copy integration is an important part of modern Data 360 architecture. Instead of creating a traditional duplicate copy of external data, supported zero-copy integrations allow Data 360 to work with data in external platforms.
Why zero-copy matters
- Can reduce the need for traditional data duplication.
- Can help organizations work with existing warehouse and lake investments.
- Can simplify certain integration architectures.
- Can provide access to external data for Salesforce use cases without moving every dataset into Salesforce.
- Can support data activation and AI use cases when the relevant integration is configured and supported.
Salesforce currently highlights zero-copy connections with platforms including Snowflake, Databricks, Google BigQuery, and AWS data sources.
Data Unification and Identity Resolution in Data 360
Connecting data is only the first step. Organizations also need to determine when records from different systems represent the same customer, account, or individual.
Data 360 supports identity resolution so organizations can create unified profiles by matching and reconciling customer identities across data sources.
Match
Identify records that may represent the same individual or organization.
Reconcile
Resolve information from different systems into a more consistent customer representation.
Unify
Create actionable profiles that can be used across Salesforce experiences and data-driven processes.
Salesforce Data 360 Integrations for AI and Agentforce
AI quality depends heavily on the quality and context of the data available to the AI system. Data 360 is designed to provide unified and trusted enterprise data that can be used across Salesforce applications and Agentforce.
Salesforce specifically positions Data 360 as a data foundation for Agentforce, allowing AI experiences to use context from connected enterprise data.
Data → Context → AI → Action
Connected Data
CRM, warehouse, application, and behavioral data.
Unified Context
Harmonized and resolved customer information.
AI
Data-grounded AI and Agentforce experiences.
Action
Workflows, recommendations, personalization, and business actions.
Salesforce Data 360 Integration Use Cases
Customer 360
Combine CRM, engagement, transaction, service, and external data to create richer customer context.
Marketing Personalization
Build segments using harmonized customer attributes and behavioral data for more relevant experiences.
Sales Intelligence
Bring external customer and business signals into sales processes and dashboards.
Service Intelligence
Give service teams additional customer context that can support case resolution and customer experiences.
Agentforce Grounding
Provide connected enterprise information that can give AI agents more relevant business context.
Data-Driven Automation
Trigger workflows and actions based on data changes, segments, insights, and business events.
Salesforce Data 360 Integration Implementation Steps
A successful Data 360 integration should begin with business objectives and data architecture rather than immediately connecting every available source.
Step 01
Define Business Objectives
Identify the business problems the integration needs to solve before selecting sources and architecture.
Step 02
Inventory Data Sources
Document Salesforce systems, warehouses, databases, applications, files, and other relevant sources.
Step 03
Select Integration Patterns
Determine whether each source should use a connector, batch ingestion, zero-copy approach, API, or another supported method.
Step 04
Map and Harmonize
Transform source data and map it to the appropriate Data 360 data model.
Step 05
Configure Identity Resolution
Establish rules for identifying and reconciling records that represent the same customer or entity.
Step 06
Activate and Monitor
Use the resulting data in business processes and continuously monitor quality, governance, and integration performance.
Salesforce Data 360 Integration Best Practices
- Start with high-value use cases. Prioritize integrations tied to measurable business outcomes instead of connecting every source immediately.
- Build a data inventory. Understand where critical customer, product, transaction, and behavioral information currently resides.
- Define data ownership. Establish authoritative sources and governance rules for important data elements.
- Prioritize data quality. Clean and standardize source information before expecting unified profiles and reliable analytics.
- Use zero-copy where appropriate. Evaluate whether supported zero-copy patterns can reduce unnecessary data movement or duplication.
- Design for security and governance. Integration architecture should account for access, privacy, compliance, consent, and data usage policies.
- Test with representative data. Validate mappings, identity resolution, transformations, and downstream use cases before scaling.
- Design for AI intentionally. If Agentforce is part of the roadmap, determine which data sources and business context agents actually need instead of exposing unnecessary information.
Common Salesforce Data 360 Integration Challenges
Fragmented Data
Customer information may be distributed across systems using different formats, identifiers, and definitions.
Poor Data Quality
Duplicate, incomplete, inconsistent, or outdated source data can reduce the value of downstream unification.
Complex Data Models
Different systems may represent the same business concepts using completely different structures.
Identity Resolution
Organizations need reliable rules for determining when records from different systems represent the same entity.
Governance
More connected data requires clear policies around access, privacy, consent, retention, and permitted usage.
AI Readiness
AI initiatives can struggle when connected data lacks quality, context, governance, or clearly defined business meaning.
Data 360 vs. Traditional Salesforce Data Integration
Build a Strong Salesforce Data Foundation
Data 360 integrations are most effective when the underlying data architecture, integration patterns, governance, and AI use cases are designed together.
Explore Kizzy’s Data Foundation for AI Services →
Salesforce Data 360 Integrations FAQs
What is Salesforce Data 360?
Salesforce Data 360, formerly Salesforce Data Cloud, is Salesforce’s data platform for connecting, preparing, harmonizing, unifying, analyzing, and activating data from Salesforce and external systems.
Is Salesforce Data Cloud now called Data 360?
Yes. Salesforce rebranded Data Cloud as Data 360 on October 14, 2025. Salesforce states that the functionality and content remained unchanged during the transition.
What can Salesforce Data 360 integrate with?
Data 360 supports connections to Salesforce applications and a wide range of external systems, including databases, data warehouses, marketing platforms, enterprise applications, and other structured or unstructured data sources. Available connectors and capabilities vary by source.
What are Salesforce Data 360 connectors?
Data 360 connectors are supported integration mechanisms for bringing data into Data 360 or, depending on the connector, sharing and activating data with external systems. Salesforce’s connector catalog lists available sources and supported data directions and methods.
What is zero-copy integration in Salesforce Data 360?
Zero-copy integration allows Data 360 to work with supported external data sources without following a traditional approach of copying all the external data into Salesforce. Salesforce currently documents zero-copy integrations with several external data platforms.
How does Data 360 support Agentforce?
Data 360 can provide connected and unified enterprise data that serves as context for Agentforce. Salesforce positions Data 360 as a data foundation for Agentforce and AI-powered experiences.
What is identity resolution in Data 360?
Identity resolution helps match and reconcile customer identities across different data sources so organizations can build more unified customer profiles.
What are the benefits of Salesforce Data 360 integrations?
Potential benefits include reducing data silos, creating unified customer context, improving personalization, supporting analytics, enabling data-driven automation, and providing enterprise data context for AI and Agentforce.
What are the challenges of Salesforce Data 360 implementation?
Common challenges include fragmented source data, data quality, complex data models, identity resolution, integration architecture, governance, security, and determining which data should actually be activated for business use cases.
Connect Your Enterprise Data With Salesforce Data 360
From Salesforce CRM and marketing data to warehouses, databases, applications, and AI use cases, the right integration architecture can turn fragmented enterprise data into actionable context.
